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1.
为推动光谱遥感在快速无损监测花生生长中的应用，明确监测花生叶面积指数（leaf area index，LAI）和地上部生物量（aboveground biomass，AGB）的最优植被指数及适宜的核心波段带宽。设置2个花生品种、4个施氮水平的花生田间试验，在不同生育时期（苗期、开花下针期、结荚期、成熟期）用Analytical Spectral Devices（ASD）公司生产的FieldSpec HandHeld 2型野外高光谱辐射仪，采集325~1075 nm范围冠层反射光谱，筛选敏感植被指数，并研究核心波段带宽对其监测叶面积指数（LAI）和地上部生物量（AGB）时精度的影响。结果显示，对花生LAI和AGB敏感的植被指数均为归一化红边指数（normalized difference red edge），即NDRE（λ790, λ720）。进一步分析这一指数的监测精度随波段带宽的变化，发现监测LAI时，核心波段带宽（bandwidth，b）在（λ790：1~33 nm，λ720：41~59 nm）范围内时能使NDRE（λ790, λ720）保持较高监测精度，其中带宽组合（λ790：33 nm，λ720：53 nm）的带宽和值最大，对核心波段带宽的要求最低，利用其构建监测模型时决定系数（determination coefficient，R2）为0.7482，利用独立试验数据检验模型时相对均方根误差（relative root mean square difference，RRMSE）为13.88%。监测花生AGB时，核心波段带宽在（λ790：1~101 nm，λ720：19~101 nm）范围内时能使NDRE（λ790, λ720）保持较高的监测精度，其中带宽和值最大的核心波段带宽组合为（λ790：89 nm，λ720：89 nm），其建模R2为0.7103，检验RRMSE为20.42%。综上，在花生整个生长进程中，可用上述两个具有不同核心波段带宽的植被指数NDRE（λ790-b33, λ720-b53）和NDRE（λ790-b89, λ720-b89）分别对LAI和AGB进行监测，监测模型为LAI = 0.0296 × exp（14.365×NDRE）和AGB = 0.6240 × exp（20.222×NDRE）。在核心波段适宜带宽上的研究结果，可以为花生长势光谱监测设备研发及评估提供参考。  相似文献   

2.
Short basal internodes are important for lodging resistance of rice(Oryza sativa L.).Several canopy indices affect the elongation of basal internodes,but uncertainty as to the key factors determining elongation of basal internodes persists.The objectives of this study were(1)to identify key factors affecting the elongation of basal internodes and(2)to establish a quantitative relationship between basal internode length and canopy indices.An inbred rice cultivar,Yinjingruanzhan,was grown in two split-plot field experiments with three N rates(0,75,and 150 kg N ha−1 in early season and 0,90,and 180 kg N ha−1 in late season)as main plots,three seedling densities(16.7,75.0,and 187.5 seedlings m−2)as subplots,and three replications in the 2015 early and late seasons in Guangzhou,China.Light intensity at base of canopy(Lb),light quality as determined from red/far-red light ratio(R/FR),light transmission ratio(LTR),leaf area index(LAI),leaf N concentration(NLV)and final length of second internode(counted from soil surface upward)(FIL)were recorded.Higher N rate and seedling density resulted in significantly longer FIL.FIL was negatively correlated with Lb,LTR,and R/FR(P<0.01)and positively correlated with LAI(P<0.01),but not correlated with NLV(P>0.05).Stepwise linear regression analysis showed that FIL was strongly associated with Lb and LAI(R2=0.82).Heavy N application to pot-grown rice at the beginning of first internode elongation did not change FIL.We conclude that FIL is determined mainly by Lb and LAI at jointing stage.NLV has no direct effect on the elongation of basal internodes.N application indirectly affects FIL by changing LAI and light conditions in the rice canopy.Reducing LAI and improving canopy light transmission at jointing stage can shorten the basal internodes and increase the lodging resistance of rice.  相似文献   

3.
为了快速、无损监测花生生长发育，建立整个生育期内花生冠层吸收性光合有效辐射（APAR）和光合有 效辐射吸收系数（FAPAR）的高光谱遥感估测模型，本试验利用高光谱遥感技术，测定沈阳地区5种不同生态类型的 花生冠层光谱数据，同期获取APAR、FAPAR；并对原始光谱数据进行logρ、1/ρ、ρ′变换，构建6种植被指数，分别与 APAR和FAPAR进行Pearson相关分析，并建立估测模型，对模型进行检验与评价。研究结果表明：4种变换形式的 光谱数据中最优波段与APAR 和FAPAR 均达极显著相关（r≥0.3969，P<0.01），以ρ′在759nm 波段处与APAR（r= 0.7574）和FAPAR（r=0.6276）的相关性最好，ρ′759nm处的高光谱参数与APAR、FAPAR建立的估测方程y = 797.3846 e271.4883x（R=0.5512，P<0.01；RE=0.1213）和y =0.756e85.21x（R=0.4204，P<0.01，RE=0.0788）拟合系数最高、预测精度较 好，估测效果很好。比值植被指数（RVI）、差值植被指数（DVI）、归一化植被指数（NDVI）、复归一化差值植被指数 （RDVI）、垂直植被指数（PVI）和修改土壤调整植被指数（MSAVI）这6种植被指数的最优波段与APAR的相关性优 于与FAPAR的相关性，MSAVI[723,761]与APAR所建立的对数函数y = 1554ln（x）+ 1631（R=0.7566，P<0.01；RE= 0.0870）和RDVI[731,764]与FAPAR建立的多项式函数y = 1.027x2 + 0.713x + 0.729（R=0.6194，P<0.01；RE=0.0699） 的模拟值和实测值均达到了极显著、预测精度较高，MSAVI对APAR和RDVI对FAPAR估测效果很好。一阶微分光 谱和植被指数可以较好地估测花生冠层APAR和FAPAR。  相似文献   

4.
为探讨遥感信息和作物生长模型在作物估产方面的优势互补特性,选取河北省藁城市冬小麦作为研究对象,采集多个关键生育时期的生理生化、农田环境、气象等数据,并获取准同步的环境减灾小卫星HJ-CCD影像数据,采用植被指数反演冬小麦叶面积指数（LAI）,基于扩展傅里叶振幅灵敏度检验法(EFAST)对WOFOST作物模型的26个初始参数进行全局敏感性分析,筛选敏感性参数,调整WOFOST模型的核心参数,利用查找表优化算法构建基于WOFOST模型和遥感LAI数据同化的区域尺度冬小麦单产预测模型,并定量预测区域冬小麦单产水平。结果表明,增强型植被指数（EVI）是遥感反演LAI的最佳植被指数(开花期建模r=0.913,RMSE=0.410,灌浆期建模r=0.798,RMSE=0.470),预测能力最强(开花期r=0.858,RMSE=0.531,灌浆期r=0.861,RMSE=0.428);筛选出6个待优化参数,即TSUM1、SLATB1、SLATB2、SPAN、EFFTB3和TMPF4;产量预测精度r=0.914,RMSE=253.67 kg·hm-2,找到了待优化参数的最佳取值,最终完成了单产模拟。  相似文献   

5.
利用单一植被指数估测叶面积指数存在高光谱遥感丰富的波段信息易丢失和外界因素干扰大的缺点,但若将波段信息全部引入模型又会增加建模难度。为解决利用多波段信息估测叶面积指数的问题,利用主成分分析法(PCA)对光谱数据进行降维,之后将提取的主成分与最小二乘支持向量机(LS-SVM)模型相结合,构建冬小麦叶面积指数的高光谱估测模型,并与以4类植被指数作为LS-SVM输入参数建立的模型进行比较。结果表明,以主成分作为LS-SVM模型的输入参数建立的模型精度最高,模型检验集R2为0.71,检验集RMSE为0.56,估测结果较使用植被指数作为输入参数建立的模型精度高,稳定性好。该方法可为利用多波段信息进行大范围冬小麦叶面积指数的无损测定提供参考。  相似文献   

6.
利用微波遥感反演植被参数往往受到植被分布不均、稀疏植被覆盖、地表裸土等因素影响,导致微波遥感用于农业参数估计的效果不佳。为解决微波遥感反演地表植被参数的问题,本研究在原有的水云模型基础上引入植被覆盖度以及裸土对于雷达后向散射系数的直接作用信息,提出一种改进的水云模型,并充分考虑地表植被的覆盖分布情况,结合地面实测数据及RADARSAT-2雷达数据对改进模型进行验证,然后根据改进模型通过查找表法反演出植被含水量,最后利用叶面积指数与植被含水量的经验关系间接得到叶面积指数的估测值。结果表明,改进的水云模型对后向散射系数的模拟精度比原有的水云模型精度高,模拟的决定系数在HH和VV极化时分别为0.850和0.739,均方根误差分别为0.918dB和1.475dB。由此可见,改进的模型对研究区植被条件更为敏感,能够较好地分离出植被与土壤信息对雷达后向散射系数的影响,同时利用其反演得到的叶面积指数精度较高,决定系数达到0.841,均方根误差为0.233。  相似文献   

7.
为了丰富大田尺度下冬小麦叶面积指数的遥感估算方法并提高估算精度,以关中地区冬小麦为对象,基于Sentinel-2多光谱卫星数据与地面同步观测的冬小麦叶面积指数样点数据,应用偏最小二乘回归(PLSR)、反向传播神经网络(BPNN)和随机森林(RF)法构建冬小麦叶面积指数估算模型,进行区域冬小麦叶面积指数遥感反演。结果表明,Sentinel-2多光谱卫星影像中心842nm近红外B8波段与冬小麦叶面积指数相关性最好,样本总体相关系数为0.778;植被指数中反向差值植被指数(IDVI)与冬小麦叶面积指数相关性最好,样本总体相关系数为0.776。各种估算模型中LAI-RF模型预测效果最佳,r~2为0.72,RMSE为0.53,RE为16.83%。基于LAI-RF估算模型,应用Sentinel-2多光谱卫星数据较好地反演了研究区冬小麦叶面积指数区域分布,其结果总体上与地面真实情况接近,说明以Sentinel-2卫星影像数据建立LAI-RF估算模型,可应用于区域冬小麦LAI反演制图。  相似文献   

8.
冬小麦叶面积指数的品种差异性与高光谱估算研究   总被引：2，自引：0，他引：2  
为给小麦叶面积指数（LAI）的高光谱估算提供技术支持,基于2年大田试验,以4个河南主推品种为材料,对小麦LAI和冠层光谱变化特点、估算模型及其品种间的差异等进行了系统分析。结果表明,在生育期内不同冬小麦品种冠层光谱反射率的变化与LAI变化有差异;在相同LAI下,不同冬小麦品种的光谱曲线存在差异。利用400~900 nm范围内冠层光谱反射率的任意两波段组合的比值光谱指数（RSI）、归一化差值光谱指数（NDSI）和差值光谱指数（DSI）所建立的单品种模型以及不同品种综合模型的决定系数（r）均达到0.84以上,单品种模型的r和调整r分别较综合模型高出3.1%~4.8%和2.0%~4.2%。利用独立于建模样本以外的数据对上述模型进行检验,单品种模型预测的r较综合模型提高了0.6%~11.0%,而均方根误差降低了10.0%~37.0%。因此,在利用高光谱遥感技术估算冬小麦LAI时,可以通过建立单品种模型来提高估算精度。  相似文献   

9.
Vegetation indices are widely used as model inputs and for non‐destructive estimation of biomass and photosynthesis, but there have been few validation studies of the underlying relationships. To test their applicability on temperate fens and the impact of management intensity, we investigated the relationships between normalized difference vegetation index (NDVI), leaf area index (LAI), brown and green above‐ground biomass and photosynthesis potential (PP). Only the linear relationship between NDVI and PP was management independent (R2 = 0·53). LAI to PP was described by a site‐specific and negative logarithmic function (R2 = 0·07–0·68). The hyperbolic relationship of LAI versus NDVI showed a high residual standard error (s.e.) of 1·71–1·84 and differed between extensive and intensive meadows. Biomass and LAI correlated poorly (R2 = 0·30), with high species‐specific variability. Intensive meadows had a higher ratio of LAI to biomass than extensive grasslands. The fraction of green to total biomass versus NDVI showed considerable noise (s.e. = 0·13). These relationships were relatively weak compared with results from other ecosystems. A likely explanation could be the high amount of standing litter, which was unevenly distributed within the vegetation canopy depending on the season and on the timing of cutting events. Our results show there is high uncertainty in the application of the relationships on temperate fen meadows. For reliable estimations, management intensity needs to be taken into account and several direct measurements throughout the year are required for site‐specific correction of the relationships, especially under extensive management. Using NDVI instead of LAI could reduce uncertainty in photosynthesis models.  相似文献   

10.
New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice   总被引：17，自引：0，他引：17  
Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)>0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.  相似文献   

11.
以杂交水稻为研究对象,进行两因素裂区试验,主区为品种,副区为施氮水平,分析了4个植被指数（VIs）分别与叶片氮素含量（LNC）、叶片氮素积累量（LNA）和地上部氮素积累量（APNA）之间的相关性,并建立了以VIs为自变量的氮素营养诊断模型。结果表明,4个VIs和LNC、LNA之间均存在决定系数大于0.7的波段区域且波段区域一致,4个VIs和APNA之间的决定系数均较低,仅在0.2左右;比值植被指数（RVI）和LNC之间的决定系数最大值为0.886,对应的波段组合为 694 nm和763 nm;垂直植被指数（PVI）和LNC之间的决定系数最大值为0.869,对应的波段组合为 864 nm和483 nm;差值植被指数（DVI）和LNC之间的决定系数最大值为0.883,对应的波段组合为1 292 nm和1 258 nm;归一化植被指数（NDVI）和LNC之间的决定系数最大值为0.881,对应的波段组合为1 296 nm和1 220 nm。最佳的氮素营养诊断模型为叶片氮素含量诊断模型,其模型表达式为LNC=1E+03NDVI2- 132.55NDVI+3.72,建模集R2、RMSE和RE分别为0.879、0.357%和16.267%,测试集R2、RMSE和RE分别为0.895、0.331%和15.136%。  相似文献   

12.
新型植被指数及其在水稻叶面积指数估算上的应用   总被引：8，自引：0，他引：8  
叶面积指数LAI不仅是陆表植被系统的一个重要属性,而且是全球水平衡、碳循环等模型中的重要输入参数。首先通过使用水稻小区试验冠层光谱数据模拟Landsat 5卫星蓝、绿、红光波段;其次分析了各个波段对LAI的敏感性;然后分析了由这个3个波段的所有组合分别代替常规NDVI中的红光波段构成的VNDVI对LAI变化的反应和对LAI的估算能力;最后使用不同条件下的水稻数据进行验证。结果表明,在不同的LAI范围,红绿蓝光3个波段对LAI有不同的敏感性。当LAI<3时,红蓝光波段敏感性较高。虽然这时绿光波段的敏感性不如红蓝光波段,然而绿光波段在更大的范围对LAI都有相当的敏感性。当采用红绿蓝光波段的各种组合构成植被指数时,如果要使这些植被指数不出现饱和现象,并使对LAI的敏感性有意义,其前提是要求这个波段或是波段组合的值要大于0.024,即VNDI(visible NDVI)公式中的VIS>0024,否则将可能产生饱和现象,而使LAI估算准确度降低。综合比较所有由红绿蓝光波段各种组合构成的植被指数对LAI的估算能力,认为GNDVI和GBNDVI与LAI有比较好的关系。使用其他条件下的水稻数据对各种NDVI的LAI估算能力进行了验证,仍然得到了同样的结论。可见,GNDVI和GBNDVI在估算LAI时确实比传统NDVI具有更好的效果。  相似文献   

13.
[目的]为探究无人机数码影像监测水稻叶面积指数(Leaf area index,LAI)的可行性,明确利用无人机数码影像监测水稻LAI的最佳时期,构建基于无人机数码影像的水稻LAI监测模型.[方法]本研究基于不同品种和施氮量的水稻田间试验,于分蘖期、拔节期、孕穗期、抽穗期和灌浆期测定水稻LAI,同步使用无人机搭载数码相...  相似文献   

14.
Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI–LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI–LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI–LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI–LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.  相似文献   

15.
为及时准确高效监测小麦叶面积指数（leaf area index,LAI）,获取了冬小麦挑旗期和开花期地面实测光谱与无人机高光谱遥感影像数据,并基于查找表建立PROSAIL辐射传输模型得到冬小麦冠层模拟光谱数据,利用数学统计回归模型与偏最小二乘回归法分别构建冬小麦LAI单变量、多变量预测模型,以实测LAI数据对预测结果进行精度评价,将最佳预测模型应用于无人机高光谱影像以分析LAI空间分布情况。结果表明,冬小麦各生育时期的预测模型均具有较高的预测精度,单变量预测模型和多变量预测模型的决定系数分别为0.598~0.717和0.577~0.755,其中以基于植被指数的多变量预测模型表现最优,其在开花期的验证精度最高,RMSE和MAPE分别为0.405和12.90%。在LAI空间分布图中,开花期预测效果优于挑旗期,各试验小区的LAI分布较为均匀。  相似文献   

16.
Accurate estimation of grassland biomass has been a central focus due to its importance in ecosystem processes and carbon cycles. This study aimed to examine whether the performance of soil‐adjusted vegetation indices for estimating above‐ground green biomass was better than that of soil‐unadjusted vegetation indices in arid and semi‐arid grasslands. Above‐ground green biomass in desert steppe of Inner Mongolia and corresponding moderate‐resolution imaging spectroradiometer (MODIS) surface reflectance 8‐day composite MOD09Q1 data were collected during late September of 2013. Results showed that soil‐adjusted SAVI (soil‐adjusted vegetation index), MSAVI (modified soil‐adjusted vegetation index), OSAVI (optimized soil‐adjusted vegetation index), TSAVI (transformed soil‐adjusted vegetation index), ATSAVI (adjusted transformed soil‐adjusted vegetation index) and PVI (perpendicular vegetation index) did not improve estimation accuracy over soil‐unadjusted simple ratio (SR) and normalized difference vegetation index (NDVI), due to low green vegetation cover (<30%) in the study area. Our results suggest that these soil‐adjusted vegetation indices may be not suitable for describing green vegetation information in arid and semi‐arid grasslands with low green vegetation cover (<30%).  相似文献   

17.
玉米生长模型MCSODS对气候变化的适应性检验   总被引：1，自引：0，他引：1  
赵巧丽  郑国清 《玉米科学》2012,20(3):148-152
利用河南省温县多年玉米栽培数据资料对玉米生长模型MCSODS的系统参数进行调试,使品种模拟参数符合当地情况。采用秋常、秋凉、秋暖不同年型下典型年份的玉米栽培资料检验玉米生长模型对气候变化的适应性。结果显示,不同年型下生育期的模拟误差根均方差(RMSE)在秋常条件下为2.06,吻合度最高;秋凉、秋暖年RMSE分别为2.32和3.45。产量模拟误差RMSE为494.8 kg/hm2,模拟效果一致性较好。2006、2007年玉米田间实测数据对系统叶面积指数和地上生物量模拟的检验结果显示,郑单958和浚单20叶面积指数的RMSE分别为0.441 0和0.402 2,地上干物重RMSE分别为1 683.0 kg/hm2和898.5 kg/hm2,模拟结果良好。  相似文献   

18.
模拟多光谱卫星传感器数据的冬小麦白粉病遥感监测   总被引：1，自引：0，他引：1  
为了解利用遥感技术快速大范围监测小麦白粉病病害情况的可行性,以Landsat5TM波段响应函数为基础,将地面实测冠层高光谱数据模拟为TM多光谱数据,从而分析卫星传感器多光谱波段对病害的响应情况,并构建多光谱指数(PMSI)估测白粉病严重度。在此基础上,采用2010年星-地配套数据对PMSI估测精度进行验证。结果表明,PMSI能够较准确地反映冬小麦白粉病发生的程度,获得较理想的病情严重度反演精度(r2=0.475,RMSE=0.129)。因此采用多光谱卫星遥感影像在小麦大面积种植区域进行病害监测具有应用潜力。  相似文献   

19.
为探讨基于无人机RGB影像实现对小麦叶面积指数(leaf area index, LAI)和产量估算的可行性,设置不同生态点、品种和氮素处理的小麦田间试验,应用大疆精灵4 Pro无人机获取小麦拔节期、抽穗期、扬花期和灌浆期4个主要生育时期的RGB高时空分辨率影像,并同测定小麦LAI。采用相关性分析筛选出不同生育时期对LAI敏感的光谱与纹理特征集,并借助随机森林(random forest, RF)、偏最小二乘回归法(partial least squares regression, PLSR)、BP神经网络(back propagation neural network, BPNN)和支持向量机(support vector machine, SVM)分析方法,筛选出小麦不同生育时期最优的LAI估测模型。基于不同生育时期的光谱与纹理特征以及时期特征集,进一步建立产量预测模型,并在不同生态点验证叶面积估算模型与产量预测模型的普适性。结果表明,基于RF的LAI估测模型的验证精度最高,4个生育时期的均方根误差(root mean square error, RMSE)分别为2.26、1.44...  相似文献   

20.
郭涛  颜安  耿洪伟 《麦类作物学报》2020,40(9):1129-1140
为快速、准确地估测不同生育时期小麦品种（系）株高与叶面积指数（LAI）表型性状，基于各生育时期小麦品种（系）数字正射影像（digital orthophoto map，DOM）和数字表面模型（digital surface model，DSM），分别构建不同生育时期株高估测模型和光谱指数LAI估测模型。借助一元线性回归、多元逐步回归（SMLR）和偏最小二乘回归（PLSR）分析方法，并采用决定系数（r）、均方根误差（RMSE）和归一化均方根误差（nRMSE）综合性评价指标，筛选出小麦不同生育时期最优的株高和LAI估测模型。结果表明，（1）全生育期株高估测效果最好，模型预测值与实测值高度拟合（r、RMSE、nRMSE分别为0.87、5.90 cm、9.29%）；在各生育时期中，灌浆期模型预测精度较好，成熟期预测精度最差，r分别为0.79和0.69。（2）所选的18种光谱指数与LAI相关性均较好，其中BGRI、RGBVI、NRI和NGRDI的相关系数达到极显著水平，且各时期三种回归估测模型均表现出较高的稳定性和拟合效果，其中SMLR回归模型对各生育时期LAI预测精度最好，其拔节期、孕穗期、扬花期、灌浆期和成熟期的预测集r分别为0.68、0.57、0.61、0.68和0.53。这说明，基于无人机获取的不同生育时期小麦DSM影像提取株高，并运用18种光谱指数构建LAI估测模型方法是可行的。  相似文献   

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